Análise paramétrica da carbonatação em estruturas de ...

© 2017 ALCONPAT Internacional 302 Revista ALCONPAT, Volumen 7, Número 3 (septiembre – diciembre 2017): 302 – 316

Revista de la Asociación Latinoamericana de Control de Calidad, Patología y Recuperación de la Construcción

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eISSN 2007-6835

Citation: E. F. Félix, R. Carrazedo, E. Possan (2017), “Parametric analysis of carbonation

process in reinforced concrete structures through Artificial Neural Networks”, Revista

ALCONPAT, 7 (3), pp. 302-316, DOI: http://dx.doi.org/10.21041/ra.v7i3.245

Parametric analysis of carbonation process in reinforced concrete structures

through Artificial Neural Networks

E. F. Félix1, R. Carrazedo1, E. Possan2

*Corresponding author: [email protected]

DOI: http://dx.doi.org/10.21041/ra.v7i3.245

Received: 04/08/2017 | Accepted: 07/09/2017 | Publicated: 29/09/2017

ABSTRACT The aim of this paper is parametrically analyze the main factors that influence on the progress of

concrete carbonation front. Therefore, a numerical model was developed using Artificial Neural

Networks (ANNs), considering the Multi-Layer Perceptron class, designed in a C++ object-oriented

program. The software was fed by experimental degradation data available in the current literature.

The results obtained in the parametric analysis, besides adding knowledge to the building pathology

area, reinforce concepts already known in the literature, demonstrating the efficiency of ANNs in the

investigation of concrete carbonation.

Keywords: carbonation of concrete; time-to-corrosion initiation; Artificial Neural Network;

mathematical modelling.

_______________________________________________________________ 1 São Carlos School of Engineering, University of São Paulo, Brasil. 2 Federal University for Latin American Integration, Brasil.

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Revista ALCONPAT, 7 (3), 2017: 302 – 316

Parametric analysis of carbonation process in reinforced concrete structures through

Artificial Neural Networks

E. F. Felix, R. Carrazedo, E. Possan 303

Análise paramétrica da carbonatação em estruturas de concreto armado via

Redes Neurais Artificiais

RESUMO

O presente trabalho tem como objetivo analisar parametricamente a influência dos principais

fatores que afetam o avanço da carbonatação em estruturas de concreto. Para tal, desenvolveu-se

um modelo numérico empregando Redes Neurais Artificiais (RNAs) do tipo Multi-Layer

Perceptron, sendo concebido em linguagem orientada a objetos C++, o qual foi testado com

dados reais de degradação disponíveis na literatura. Os resultados obtidos na análise paramétrica

reforçam conceitos já conhecidos na literatura, demonstrando a eficiência de RNAs no estudo da

carbonatação do concreto, além de agregar conhecimento à área de patologia das construções.

Palavras chave: carbonatação do concreto; tempo de iniciação da corrosão; Redes Neurais

Artificiais;

Análisis paramétrico de la carbonatación en estructuras de hormigón por

Redes Neuronales Artificiales

RESUMEN

El presente estudio tiene como objetivo analizar paramétricamente los principales factores que

influyen en el avanzo de la carbonatación de las estructuras de hormigón. Por lo tanto, se

desarrolló un modelo numérico utilizando Redes Neuronales Artificiales (RNAs o NeuroRed),

del tipo Multi-Layer Perceptron, desarrollada en lenguaje orientado a objetos C++, la cual fue

probada por datos de degradación reales disponibles en la literatura. Los resultados obtenidos en

el análisis paramétrico refuerzan conceptos ya conocidos en la literatura, demostrando la

eficiencia de las RNAs en el estudio de la carbonatación del concreto, además aportando

conocimientos en el área de patología de las construcciones.

Palabras clave: carbonatación del hormigón; tiempo de iniciación de la corrosión; Redes

Neuronales Artificiales; modelado matemático.

1. INTRODUCTION

Concrete reinforcement (Rebars) corrosion is the pathology with highest occurrence index in

reinforced concrete structures (Taffese et al., 2013; Kari et al., 2014; Possan, Andrade, 2014;

Andrade et al., 2017). As an example, this index varies from 14 a 64 % in Brazil, according to the

region (Dal Molin, 1988; Andrade, 1992; Aranha 1994).

Carbon dioxide (CO2) ingress leads to reduction of calcium hydroxide (Ca(OH)2) from the porous

concrete matrix and, as a consequence, concrete pH decreases from 13 to approximately 8, letting

rebars susceptible to corrosion (Bakker, 1988; Chang et al., 2006). According to Possan et al.

(2017), the increasing CO2 emissions in the atmosphere worldwide with cities development brings

several consequences to concrete structures in urban environments. The life cycle of the

structures are affected by the elevation of CO2 emissions in the environment as the rate of

carbonation increases, reducing their durability.

There are nowadays several works that explain and model carbonation of concrete. Until mid-

1980s, prediction of carbonation depth were obtained by linear and non-linear regressions, based

in several factors, such as water/cement ratio, type of binder and exposure conditions (Izumi et




E. F. Félix, R. Carrazedo, E. Possan 304

al., 1986; Kobayashi et al., 1990). In the following years, Papadakis et al. (1991), Ishida et al.

(2001) e Maekawa et al. (2003) included physico-chemical formulations related to the hydration

reaction of the cement paste and the CO2 dissolution in the concrete porous matrix in their

models, which enabled more accuracy in the determination of the carbonation front. However,

Possan (2010) point out that these models requires resolution of great complexity equations that

govern the diffusion of CO2 in concrete, and hard to find parameters, such as the diffusion

coefficient of carbon dioxide.

Use of computational tools, such as Artificial Neural Networks (ANNs), is a reliable alternative

to overcome hardships imposed by the modeling of carbonation of concrete due to is ability to

map and to model complex non-linear problems, without knowing all phenomena involved

(Braga et al., 2000, Lu et al., 2009; Kwon et al., 2010; Güneyisi et al., 2014; Taffese et al., 2015;

Félix, 2016).

In this study, we analyze several factors on the carbonation phenomenon, such as relative air

humidity, CO2 concentration, concrete composition, cement type, admixtures, exposure

conditions to rain, and compressive strength of concrete. A prediction model of the carbonation

depth is obtained through Multilayers Perceptron ANN and Backpropagation learning algorithm.

Results show the ANN potential to model the depth of carbonation in concrete.

2. CARBONATION OF CONCRETE

Carbonation of concrete is a physical-chemical reaction that leads to the reduction of capillary

porosity and affects the equilibrium of pore water content. Corrosion of rebar in reinforced

concrete is also a consequence (Neville, 1997). According to Vesikari (1988) and Hamada

(1969), the depth of carbonation in concrete increases over time (Figure 1), as a function of

several intrinsic parameters and the environment.

Figure 1. Schematic representation of the carbonation of concrete.

Adapted from Possan (2010).





There are several works in which carbonation and its influence factors are described, such as

Hamada, 1969; Parrot, 1987; Helene, 1993; Houst et al., 2002; Pauletti et al., 2007; Possan 2010;

Talukdar et al., 2012. Pauletti et al. (2007) and Possan (2010) point out that the influence

parameters in the carbonation of concrete are related to (i) environmental conditions:

temperature, relative air humidity and CO2 concentration; (ii) concrete: mix design, quality of

execution and curing, use of admixtures, and chemical composition of the binder; and (iii)

exposure conditions: internal, external environment and rain protection. All these factors must be

evaluated both in the study of carbonation phenomenon and in its modeling. In the present work

an AAN model is proposed to evaluate the depth of carbonation in concrete considering, as input

parameters, relative humidity, CO2 concentration, concrete compressive strength, cement type,

exposure conditions, use and mix design of admixtures, and age of concrete.

3. CARBONATION PREDICTION MODEL THROUGH ANN

The proposed methodology is divided in two stages: i) development of a prediction model of

depth of carbonation in concrete using ANN; and ii) parametric analysis of the variables

employed by the model.

3.1 Model Development

We based our model on the Multilayer Perceptron model trained by Backpropagation Momentum

algorithm. The methodology used to obtain the model is presented in flowchart of Figure 2.

Figure 2. Flowchart of the prediction model of depth of carbonation in concrete





In the first stage, the database is set up to cover all input variables (relative humidity, CO2

concentration, concrete compressive strength, cement type, exposure conditions, use and mix

design of admixtures, and age of concrete). The database is composed by experimental results

from Meira et al. (2006) and Vieira et al. (2009), and by Possan (2010) focus group, using

respectively 179 and 100 data points. Some input variables were converted to numbers in order to

be properly associated to the AAN, such as cement type (CP II-E, CP II-F, CP II-Z, CP III, CP IV

and CP V ARI), numbered from 1 to 6. Exposure conditions was also represented by 1.30, 1.00

and 0.65 when exposed to indoor environment, external environment yet protected from rain and

unprotected from rain, respectively, as established by Possan (2010). This process defined the

model applicability and boundaries for input variables, as presented in Table 1.

Table 1. Database boundaries.

Input Variable Boundaries / Domain

Cement Type [CP II-F1; CP II-Z2; CP II-Z3; CP III4; CP IV5; CP V6]

Relative Humidity (%) [30 - 90]

Exposure conditions [1.30, 1.00, 0.65]

Content of additions (%) [0-30]

CO2 concentration (%) [0.01-3.0]

Compressive strength (MPa) [20-90]

Age of concrete (years) [0-60] 1 CP II F: Portland cement composite with filler - NBR 11578. There is no equivalent in ASTM. 2 CP II Z: Portland cement composite with Pozzolan - NBR 11578. Pozzolan-modified Portland - ASTM C 595. 3 CP II E: Portland cement composite with Slag - NBR 11578. Slag-modified Portland - ASTM C 595. 4 CP III: Portland cement composite with blast furnace - NBR 5735. Portland blast furnace slag - ASTM C 595. 5 CP IV: Portland pozzolan cement - NBR 5736. Portland pozzolan - ASTM C 595. 6 CP V ARI: Portland cement high initial strength - NBR 5733. Portland with high early strength - ASTM C 150.

The boundaries or domain defines limits to use the model, since AAN are unable to extrapolate

results, and it is only possible to map and to train an ANN within its domain (Braga et al., 2000).

AAN requires splitting the entire database in three smaller databases, one for training, other to

validate, and the last to evaluation. Figure 3 shows the amount of data allocated in each database.

Figure 3. Amount of data allocated in each database.

Each database (training, validation and evaluation) are used in a step of the process of modelling

with AAN. The first database is responsible to train the network, through each pair of





input/output. The second database is responsible to validate and to certify the trained network.

The third and last database is responsible to test and to check the model capabilities.

AAN trained was then classified by topology, activation function and learning rate of the train

algorithm, as described by Félix (2016).

We created 1200 ANN’s with different learning rates (0.1, 0.2, 0.3 and 0.4), input entries (4, 5

and 7 perceptron’s), number of hidden layers (one or two), and number of perceptron’s in the

hidden layer (from 0 to 9). With all possible combinations, resulted in 1200 ANN’s (4*3*10*10).

See figure 4 for details.

Figure 4. Tested topologies and input entries.

We adopted the root mean square error (RMSE) between depths of carbonation measured and

depths of carbonation evaluated by the trained network as convergence criteria, according to

Equation (1).

2

1

1 n

i m

i

RMSE x xn

(1)

where n is the number of outputs, xi is the value provided by the network for the i-th output, and

xm is the average of the values from all outputs.

ANN training is made by the package Project-Yapy (Konzen et al., 2011), provided in C++.

ANN performance was evaluated by the following parameters: correlation coefficient (R²), root

mean square error (RMSE), maximum error (Emax, largest error provided), and minimum error

(Emim, smallest error provided). These parameters were evaluated both in the training stage and in

the validation stage. In the test stage, these parameters were also used to access the performance

of the network.

After ordering by their performance, it was possible to select the network that could better

represent the carbonation of concrete. Figure 5 shows the selected network, containing three

layers of perceptrons. The first layer has seven neurons, responsible for input. The second (or

hidden) layer has four neurons, which are responsible to processing information, and the last

layer has a single neuron, responsible for output – depth of carbonation in concrete.





Figure 5. Chosen topologies and input entries.

Correlation charts between the carbonation depths modeled by the program (modeled depth) and

the natural carbonation depths (observed depth, provided by Possan (2010)) are shown in Figures

6(a) and 6(b).

Figure 6(a). Training correlation. Figure 6(b). Validation correlation.

A complete description of the training process of the networks and the parameters used in the

modeling can be found in Felix (2016).

3.2 Parametric analysis of carbonation

We decided to perform a parametric analysis of the influence of the input variables on the

evaluation of the carbonation depth in concrete. The analysis was divided into four, evaluating

the influence of a single or two input variables, as shown in Figure 7.





Figure 7. Input parameters evaluated in each parametric analysis.

4. RESULTS

4.1 Validation with reference

Initially, in order to certificate the performance of the developed model, we compared our results

with others proposed models and degradation values provided by Possan (2010). Equation (2)-(6)

introduces carbonation models provided by Smolczyk (1976), Vesikari (1988), Bob & Afana

(1993), EHE (2008) and Possan (2010), respectively.

𝑦 = 𝑎. (1

√10𝑓𝑐

−1

√10𝑓𝑐𝑙𝑖𝑚

) . √52. 𝑡 (2)

𝑦 = [26. (𝑎𝑐 − 0.3)2 + 1,6] (3)

𝑦 = 150. (𝑐. 𝑘. 𝑑

𝑓𝑐

) . √𝑡 (4)

𝑦 = 𝐶𝑎𝑚𝑏 . 𝐶𝑎𝑟 . 𝑎. 𝑓𝑐𝑚𝑏 . √𝑡 (5)

𝑦 = 𝑘𝑐 . (20

𝑓𝑐

)𝑘𝑓𝑐

. (𝑡

20)

1

2

. 𝑒𝑥𝑝 [(𝑘𝑎𝑑 . 𝑎𝑑

3

2

40 + 𝑓𝑐

) + (𝑘𝐶𝑂2

. 𝐶𝑂2

1

2

60 + 𝑓𝑐

) − (𝑘𝑅𝑈 . (𝑈𝑅 − 0.58)2

100 + 𝑓𝑐

)] . 𝑘𝑐𝑒 (6)

where is y is the carbonated depth (mm), a is the rate of carbonation, fc is the concrete

compressive strength (Mpa), fclim is a limiting value for the carbonated concrete compressive

strength (MPa), t is the age of concrete (years) and ac is water/cement ratio. The input parameters

that are function of the type of binder and exposure conditions are defined using tables provided

by each author in their work. More details can be found in Félix (2016).





Some scenarios are provided in Table 2 and compared in Figure 8(a)-(d), showing the depth of

carbonation vs time.

Table 2. Test Stage – Trail Scenarios.

Scenario CO2 (%) Relative

humidity (%)

Exposure

conditions

Binder

type

Compressive

Strength (MPa)

I 0.01 70.00 Protected CP II – F 30.00

II 0.01 70.00 Protected CP III 40.00

III 0.01 65.00 Unprotected CP IV 40.00

IV 0.01 65.00 Unprotected CP V 40.00

OBS.: Time of analysis: 60 years; No chemical addition is considered.

Em todos os cenários o teor de adição (no concreto) é zero e o tempo de análise é de 60 anos.

(a). Scenario I. (b). Scenario II.

(c). Scenario III. (d). Scenario IV.

Figure 8. Test Stage – Trail Scenarios.

The results show the applicability of the model and that the proposed model is an efficient tool

for estimating the depth of carbonation in concrete.

4.2 Parametric Analysis

Figure 9 shows depth of carbonation in concrete after 50 years obtained in the proposed model

varying only the type of binder and the compressive strength. In this simulation, we considered

an environment protected from rain, with 65% of relative humidity, 0.04% CO2, and no additions

in the concrete production.





Figure 9. Depth of carbonation as a function of type of binder and compressive strength.

One may notice in Figure 9 that concretes produced with CP III and CP IV present greater depth

of carbonation, notably with low compressive strength concretes. Jiang et al. (2000) and Possan

(2010) noticed a negative influence of additions on the depth of carbonation, due to the reduction

of the alkali reserve when the concrete is produced with CP III and CP IV, which have high

levels of slag (from 35 to 70%) and pozzolan (from 15 to 50%) in their compositions,

respectively. CP II-E and CP II-Z are also composed cements (with slag and pozzolan,

respecively), however with lower levels of admixtures. That would explain the lower depth of

carbonation in concrete produced with CP II-E and CP II-Z than CP III and CP IV.

Figure 10. Depth of carbonation as a function of levels of additions and compressive strength.





One may notice in Figure 10 the influence of additions content (silica fume) on the depth of

carbonation. It is observed that the depth of carbonation is barely affected by the addition in

concretes with higher compressive strength (40, 50 and 60 MPa). Is also noted that the higher the

addition content, greater is the depth of carbonation for concretes with lower compressive

strength than 40 MPa. Kulakowski et al. (2009) report that, in concrete with higher compressive

strengths (greater than 30 MPa), the CO2 intake is smaller due to porosity, even for concretes

with low alkaline reserves. The authors point out that, for concrete with compressive strength

greater than 40 MPa, the depth of carbonation is independent of additions and type of cement. In

the case of lower compressive strength, the presence of additions increases the depth of

carbonation, and the alkali reserve effect predominates (Kulakowski et al., 2009).

Figure 11. Depth of carbonation as a function of relative humidity and exposure conditions.

Figure 11 shows the depth of carbonation in a 50 years old concrete obtained in the proposed

model when relative humidity and the environment exposure conditions are modified. In this

simulation, we considered a concrete structure with compressive strength of 30 MPa, CP III, 0.04

% CO2, and no additions in the concrete production.

One may notice in Figure 11 that the depth of carbonation reach maximum when relative

humidity is close to 60%. Parrot (1987), Neville (1997) and Possan (2010) point out that depth of

carbonation reaches its maximum value when the relative humidity is between 50 and 80%. They

also mention that the relative humidity can be considered as the environmental factor with the

greatest influence on carbonation. Possan et al. (2017) observed in a 35 years old concrete dam

that the larger the internal humidity, the lower the depth of carbonation depth. The authors also

notice that no carbonation was observed when moisture was about 100%.

Figure 12 shows the depth of carbonation as a function of the exposure conditions to different

CO2 concentrations. In this simulation, we considered a concrete structure with compressive

strength of 30 MPa, CP III, relative humidity of 65%, no additions in the concrete production,

and in an unprotected outdoor environment.

One may notice that higher degree of exposure to CO2, greater the depth of carbonation in

concrete over time. An increase of 0.1% of CO2 concentrations leads to an increase of carbonated

depth in 2.15%.





Figure 12. Depth of carbonation as a function of CO2 concentrations.

5. CONCLUSIONS

In this work, we present an Artificial Neural Network for the prediction of the depth of carbonation in

concrete structures. Results show the great potential of ANN to model the carbonation phenomenon,

considering the several types of cements commercialized in Brazil.

The multiplayer perceptron network developed in this work is capable to provide the depth of

carbonation as function of relative air humidity, CO2 concentration, concrete composition, cement

type, admixtures, exposure conditions to rain, and compressive strength of concrete.

The parametric study carried out on the developed model confirmed results described by others, such

as:

1. The carbonation decreases as the compressive strength of the concrete is increased;

2. The type of cement has a secondary influence on the carbonation phenomenon, since the

carbonation is modified by the content of admixtures present in the cement;

3. Additions only has influence on depth of carbonation on concretes with low compressive strength

(up to 60% carbonate depth), which is reduced or even eliminated in concretes with high

resistance.

4. Exposure to environments with high CO2 concentrations, such as tunnels, parking lots, urban

environment with heavy vehicle traffic, increases carbonation rate.

The results obtained in the parametric analysis demonstrate the efficiency of ANNs in the study of the

carbonation rate in concrete, improving the study of constructions pathology.

6. ACKNOWLEDGMENTS

We acknowledge the Advanced Dam Safety Study Center (CEASB), the Itaipu Technological

Park (PTI) and the Coordination for the Improvement of Higher Education Personnel (CAPES),

for the financial support.





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